Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.

By A. Bourke, J. O'Brien, G. Lyons. 2007

895 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation

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In Biomedical Engineering

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In Physical Therapy, Sports Therapy and Rehabilitation

Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “According to [51], the best baseline results are achieved by Threshold UFT method [8], which are 95.”
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information (2009, background): “Bourke [9] placed two triaxial accelerometers at the trunk and thigh, and derived four thresholds, upper and lower thresholds for both the trunk and thigh.”
Direct measurement of human movement by accelerometry. (2008, background): “showed that fall detecion with a trunk mounted accelerometer was possible with a pecificity of 100% [69].”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “The vast majority of previously developed algorithms have utilised heuristic features exploiting one or more intrinsic characteristics of a fall, such as velocity (Bourke et al 2008), acceleration (Bourke et al 2007), orientation (Hwang et al 2004) or even sound (Doukas and Maglogiannis 2008).”
Vision based hand gesture recognition for human computer interaction: a survey (2012, neutral): “4 Vision based hand gesture representations (Bourke et al. 2007)”
Fall detection using ceiling-mounted 3D depth camera (2015, background): “A lot of different approaches have been investigated to achieve reliable fall detection using inertial sensors (Bourke et al., 2007).”
Elderly Fall Detection Systems: A Literature Survey (2020, background): “Bourke et al. (2007) also found that accelerometers are regarded as the most popular sensors for fall detection mainly due to its aﬀordable cost, easy installation and relatively good performance.”
Human fall detection on embedded platform using depth maps and wireless accelerometer (2014, background): “As we can observe, the Threshold UFT method [10] achieves good results.”
A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls (2009, neutral): “The most popular solutions for automatic detection of falls are the wearable fall detectors that are based on combination of accelerometers and tilt sensors [5], [ 6 ].”
Sensor Positioning for Activity Recognition Using Wearable Accelerometers (2011, background): “In recent years, wearable accelerometers have played an important role in inferring metabolic energy expenditure [7]–[9], measuring gait parameters [10], predicting falls [11], and detecting activities of daily living [12]–[14].”
The Promise of mHealth: Daily Activity Monitoring and Outcome Assessments by Wearable Sensors (2011, background): “94,96,97 In general, these devices have only been put to use in a laboratory setting.”
Wearable Sensors and Systems (2010, background): “During the past decade, a number of devices for fall detection have been developed by researchers [ 56 ]–[60], and fall-detection devices have been introduced on the market.”